Rendering HTML

import plotly
plotly.offline.init_notebook_mode()

Loading the Iris Dataset

from sklearn.datasets import load_iris
import pandas as pd

data_iris = load_iris()
df_iris = pd.DataFrame(data_iris.data,columns=data_iris.feature_names)
df_iris

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2
...	...	...	...	...
145	6.7	3.0	5.2	2.3
146	6.3	2.5	5.0	1.9
147	6.5	3.0	5.2	2.0
148	6.2	3.4	5.4	2.3
149	5.9	3.0	5.1	1.8

150 rows × 4 columns

Redefine 2 classes for the target variable: virginica and non-virginica

df_iris['y'] = data_iris.target_names[data_iris.target] == 'virginica'
df_iris.head()

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	y
0	5.1	3.5	1.4	0.2	False
1	4.9	3.0	1.4	0.2	False
2	4.7	3.2	1.3	0.2	False
3	4.6	3.1	1.5	0.2	False
4	5.0	3.6	1.4	0.2	False

Explore the Data

Table with descriptive statistics for Virginica class

virginica = df_iris[df_iris['y'] == True]
not_virginica = df_iris[df_iris['y'] == False]

virginica.describe()

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	50.00000	50.000000	50.000000	50.00000
mean	6.58800	2.974000	5.552000	2.02600
std	0.63588	0.322497	0.551895	0.27465
min	4.90000	2.200000	4.500000	1.40000
25%	6.22500	2.800000	5.100000	1.80000
50%	6.50000	3.000000	5.550000	2.00000
75%	6.90000	3.175000	5.875000	2.30000
max	7.90000	3.800000	6.900000	2.50000

Table with descriptive statistics for Not Virginica class

not_virginica.describe()

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	100.000000	100.000000	100.000000	100.000000
mean	5.471000	3.099000	2.861000	0.786000
std	0.641698	0.478739	1.449549	0.565153
min	4.300000	2.000000	1.000000	0.100000
25%	5.000000	2.800000	1.500000	0.200000
50%	5.400000	3.050000	2.450000	0.800000
75%	5.900000	3.400000	4.325000	1.300000
max	7.000000	4.400000	5.100000	1.800000

Histplot per feature for each of the two classes

import seaborn as sns
import matplotlib.pyplot as plt

for col in df_iris.columns[0:4]:
    sns.histplot(data=df_iris,hue='y',x=col,bins=10,palette='viridis')
    plt.legend(['Not Virginica','Virginica'])
    plt.show()

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning:

use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\seaborn\_oldcore.py:1075: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

Correlation matrix between the four features

df_iris_corr = df_iris[df_iris.columns[0:4]].corr()
print(df_iris_corr)
plt.figure(figsize=(12,10))
sns.heatmap(df_iris_corr, annot=True)
plt.title('Heatmap of Correlation Matrix of Iris Dataset')
plt.show()

                   sepal length (cm)  sepal width (cm)  petal length (cm)  \
sepal length (cm)           1.000000         -0.117570           0.871754   
sepal width (cm)           -0.117570          1.000000          -0.428440   
petal length (cm)           0.871754         -0.428440           1.000000   
petal width (cm)            0.817941         -0.366126           0.962865   

                   petal width (cm)  
sepal length (cm)          0.817941  
sepal width (cm)          -0.366126  
petal length (cm)          0.962865  
petal width (cm)           1.000000  

Additional graphs

Pie Chart for Count of Virginica & Non Virginica

import plotly.express as px

custom_colors = ['#1f77b4', 'skyblue', 'cornflowerblue']

# Create a pie chart with custom colors
fig_pie = px.pie(values=df_iris['y'].value_counts(),
                 names=['Not Virginica','Virginica'],
                 title='Pie Chart of Species Counts',
                 color_discrete_sequence=custom_colors)

# Show the pie chart
fig_pie.show()

Pie Chart of Species Counts

Observation :

• 33.3% of the data are of Virginica species and rest of them are Not Virginias.

fig_boxplot = px.box(df_iris, x="y", y="sepal length (cm)", title="Box Plot of Sepal Length by Species",
                     color='y',  # Set box color based on Species              
                     color_discrete_sequence=custom_colors, # Custom color sequence
                    )

# Update layout
fig_boxplot.update_layout(xaxis_title="Species", yaxis_title="Sepal Length",xaxis=dict(showticklabels=False))

# Update legends
fig_boxplot.update_traces(name = "Virginica", selector=dict(name="True"))
fig_boxplot.update_traces(name = "Non Virginica", selector=dict(name="False"))

# Show the box plot
fig_boxplot.show()

d:\AI & ML\ML FOUNDATION\LABS\CSCN8010\venv\CSCN8010_classic_ml\Lib\site-packages\plotly\express\_core.py:1985: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.

Box Plot for Sepal Length

• From the above visualization, it is evident that the species Virginica exhibits a higher average value of Sepal Length compared to the other species.

Split the data to a train set (120 records), a validation set (15 records) and a test set (15 records).

from sklearn.model_selection import train_test_split

X_train, X_old, y_train, y_old = train_test_split(df_iris[df_iris.columns[0:4]], df_iris['y'], test_size=0.2, random_state=42)

X_val, X_test, y_val, y_test = train_test_split(X_old, y_old, test_size=0.5, random_state=42)